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--- |
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license: mit |
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base_model: microsoft/layoutlm-base-uncased |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: layoutlm-funsd-tf |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information Keras had access to. You should |
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probably proofread and complete it, then remove this comment. --> |
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# layoutlm-funsd-tf |
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This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 0.2350 |
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- Validation Loss: 0.6723 |
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- Train Overall Precision: 0.7420 |
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- Train Overall Recall: 0.7978 |
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- Train Overall F1: 0.7689 |
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- Train Overall Accuracy: 0.8134 |
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- Epoch: 7 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} |
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- training_precision: float32 |
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### Training results |
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| Train Loss | Validation Loss | Train Overall Precision | Train Overall Recall | Train Overall F1 | Train Overall Accuracy | Epoch | |
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|:----------:|:---------------:|:-----------------------:|:--------------------:|:----------------:|:----------------------:|:-----:| |
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| 1.7200 | 1.4450 | 0.2446 | 0.2479 | 0.2462 | 0.4720 | 0 | |
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| 1.1874 | 0.8977 | 0.5707 | 0.6563 | 0.6105 | 0.7371 | 1 | |
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| 0.7800 | 0.7307 | 0.6355 | 0.7471 | 0.6868 | 0.7774 | 2 | |
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| 0.5924 | 0.6328 | 0.6774 | 0.7817 | 0.7258 | 0.8045 | 3 | |
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| 0.4601 | 0.6043 | 0.7228 | 0.7878 | 0.7539 | 0.8133 | 4 | |
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| 0.3731 | 0.6318 | 0.7220 | 0.7988 | 0.7585 | 0.8099 | 5 | |
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| 0.2933 | 0.6364 | 0.7358 | 0.8023 | 0.7676 | 0.8145 | 6 | |
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| 0.2350 | 0.6723 | 0.7420 | 0.7978 | 0.7689 | 0.8134 | 7 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- TensorFlow 2.15.0 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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